5 Ways AI is Shaping the Future of Software Testing

Artificial Intelligence is the hottest buzzword these days and advancement in AI allows Enterprise and industries to make smart decisions and radically transform processes. As software tests shift gears from manual to automation for embracing the speed for DevOps and digital transformation, Artificial Intelligence has emerged to be the key lever for this change.

AI working ways
AI working ways

Applications today interact with other apps through APIs, they leveraging legacy systems, and they grow in the complex from one day to the next in a nonlinear fashion. What does that mean for experts? The recent World Quality Report declared that it will help. It is now believed that the most significant solution to overcome increasing QA Challenges will be the rising trend and introduction of machine-based intelligence.

Software testing is an imperative process that makes sure client satisfaction in the app. It is fundamentally about inputs and anticipated results that blend a big amount of human as well as machine-generated data. And so, the test is a ready ground for AI.

Whether automated or manual, software testing can benefit greatly from using it, BOTs, as well as the intelligence, derived data & analytics. Test bots run automated tasks (scripts) over the Internet & perform repetitious actions. Test bots (Voice Bots and Chat Bots) also interpret, mimic and react to the written and spoken the language. Bots aren’t hard-coded and can notice the newest features in the app.

5 significant ways it is changing this dynamic:

1- Testing is becoming error-free & simple- Because the new algorithms are such significant analyzing tools, the adept software developers will no longer have to write all the scripts and examine so many data. The newest AI will also have the capability to sort through log files to improve accuracy in the specific program and to save the precious amount of time. The data results generated give developers a thorough view of the changes that must be re-enforced. It shows developers where testing is required by locating flaws in a system. Moreover, the new algorithms can also foretell future complications, which heighten efficiency during test periods.

2- Optimizing the Test Suite- One of the important flaws facing software development and testing is over-engineering those results in loss of resources, effort and time. As companies release speedy and use automation to move forth, they often struggle with huge amounts of a backlog. BOTS can also help clear the backlog and enable focus on the tests right. BOTs can identify the same/redundant and unique test cases, thus removing the duplicates and intensifying traceability.

3- Defect analysis- The intelligent approach to identify imperfection early and provide suggestions to avoid this flaw and speed up cycle time. Artificial Intelligence can accurately discover usage and failure trends to spot the most critical and less obvious faults, which allows teams to prioritize regression test cases based on dangers identified.

4- AI can eliminate extra bugs- Bugs will speedily ruin the user experience, and software test is required to halt such flaws and disruptions. When bugs are captured and caught, expert testers are left wondering how the bug went undiscovered and when the bug entered the program. With the proper use of AI the questions of when, how, and where bugs entered a system are put to rest. Testers can use the information generated by it to detect if more coding modifications will be required to control bugs from entering the program. AI is constantly testing to clear-out excessive bugs from programs.

5- Prescriptive and Predictive Analytics- BOTS thrive on data and luckily most enterprises have immense amounts of production and test data bring forth by automation suites. Artificial Intelligence can be utilized to analyze test results, identify flaws and predict the quality. It uses this data to foretell the key parameters of procedures and prescribe the best course of actions. The smart approach to detect usage and failure trends to spot the crucial, critical and less obvious faults.

AI has a world-shaking high impact on the quality function with benefits ranging from superior quality to speed to market, optimization, better coverage, traceability, and extraordinary savings on the overall cost of development.

Artificial Intelligence Permeation in Testing

Software Testing is the process that ensures the customer is satisfied with the application and provides defect-free software. It allows testing an application under some conditions where maximum threshold and risks are involved in implementation. Testing ensures the quality, output and market efficiency of the software. Here comes the Artificial Intelligence (AI), which reduces manual effort and allows machines to write and execute test codes.

About Artificial Intelligence

AI allows the machine to read and process information at a very high-speed. They intelligently react to the environment changes and can learn things at a speedy pace. Some algorithms are applied that allow machines to analyze and identify data logically. It is a probabilistic approach applied to test the application. ‘AI Robots’ are introduced that performs testing with minimal human inputs. This will improve testing efficiency and decrease failure rates.

Benefits of Artificial Intelligence in Testing

  • Improved Quality: As testing is performed automatically, with assured security, the quality will be improved. It increases the market efficiency of the applications.
  • Effective: AI theories and algorithm focuses on reliable testing methods. It ends up reducing manual effort and intensive costs.
  • Timely Feedback: Since AI testing is automated it provides quick feedback on the application. It also reports the application’s efficiency.
  • Improves Trace-ability: AI checks the error by going through the code itself and leaves no error unattended. It resolves all issue and then proceeds forward.
  • Integrated Platform: Entire AI process is based on the embedded and integrated platform to run tests. Due to this, the website is launched easily by developers.

AI Robots take less time and capable to find testing paths on their own. They can be easily maintained. The bots are trained to process the data input and performs an action intelligently, like Android Auto Assistant. These bots are strengthened with time as the AI algorithms are continuously monitored to study behavior and input patterns.

Artificial Intelligence and Automated Tool

Intellectual decisions are bases for Artificial Intelligence automation when regression testing is performed. These are built on the algorithms with data and examples. The basis of AI Automation is the system’s intellectual decision-making ability. The information gathered by AI shows the application behavior, its stability, defect area, failure pattern and so on. The Artificial Intelligence System correlates the data information with existing test suites and also auto generates the test cases or test code by following the user story acceptance criteria. It supports code-less test automation on mobile or web applications. Artificial Intelligence mainly focuses on test management.

The following inputs are required to generate the tests automatically:


  1. Relevant data for the application
  2. Test results data for pass or failed use cases
  3. Requests and their valid data, that are to be run on production as well as the test environment
  4. Builds a version control monitor (can be SVN or others)
  5. Historical data, on which AI works

The names of the tools which are available to automate testing with Artificial Intelligence are: Testim, Appvance, Functionize, Endtest, Appitools.

AI Changing Test Automation

The machine learning AI is more of the statistics-based. It has transformed the way the test automation is performed. ML algorithms recognize all the patterns to predict the trends followed by an application.

Real-time examples that are following machine learning algorithms to embed AI:

  • Smartphones are using voice recognition software (e.g.-SIRI) that allow human interactions to do some action.
  • While online shopping, like in Amazon, list of recommendations to buy comes up as per previous experience is followed by Machine learning algorithms.
  • Visual Validation Automation Testing: This is an image-based testing technique done by visual validation tool. Like Applitools, it can find the differences that may be skipped by testers. This is required to verify if UI is as expected to the application users. It ensures that the UI element has correct position, shape, size, and apt color. It also tests if any UI element is not hidden and do not overlap too. For this, testers create ML test which detects visual bugs and validate the correctness. The AI system keeps screen-shots virtually in its mind to determine the state of the application.
  • Provides more reliable Selenium Automated Test: Let’s take a scenario where there are frequent changes (e.g.: changing the ID of Web element) to the application and Selenium tests fail as the element is not found. AI tools adjust to these changes automatically using Machine Learning algorithms and find locators, rather than doing changes on selector or path for the element. These AI tools start to learn about an application and understand the relationships among parts of DOM. It doesn’t break the tests and keeps track of the changes throughout time. This makes automated tests reliable and easy to maintain.
  • API Testing with AI: Some automation tools are available to remove complexity from the API testing. AI helps to simplify the process of API Testing. ‘Smart API Test Generator’ is a plugin embedded in chrome which helps to convert manual UI tests to automated API tests using artificial intelligence. It helps in building the comprehensive strategy to perform API testing. This tool identifies API calls, and then observes pattern followed and finally analyze the relationships among them. This process flow generates API testing scenarios.
  • Running more of precise test cases: When there is some change in code, it takes much effort to analyze the minimum number of tests to be run. For this, AI & Machine Learning is used to tell the precise number of tests to run. Also, AI is significant when the tester is unable to finish test-failure triage before the next build is released. Here, Machine Learning algorithm forms the ‘fingerprint’ of failed test cases in correlation with debug logs and system. It further predicts all the duplicate failures. This makes precision testing grow.

Effect of AI testing for Continuous Delivery

Testing allows developers to figure out if the application is working as expected in the real world. AI automation testing tools increase feasibility to identify the gaps in the application. AI enables testers to view wider issues, rather than just looking into repetitive ones. It offers the testers to customize the tests with combined information. These solutions are stored in metrics which tracks the success rate and perform execution cycles. The analytics collected is used to track issues in the software development cycle. Every industry is trying to learn and use AI supported apps that can automate tasks. Organizations may face multiple testing challenges when using ML and AI for testing application quality. AI algorithms optimize test suites, provide log analytics, traceability, and rapid impact analysis and get defect analytics.